Humanitarian Risk Analytics for India: A Dual-Dataset Approach to Food Prices and Poverty

Project Vision

To analyze and model humanitarian vulnerabilities in India by integrating market-level food price data with poverty indicators, using SQL, machine learning, and AI-powered reporting.

Key Features

• SQL Workflow: Cleaned and joined market-level food price and poverty datasets
• Python Analysis: Exploratory trends, volatility detection, and clustering (KMeans + SHAP)
• Power BI Dashboard: 5 annotated pages with KPI cards, maps, outlier detection, and cluster profiles
• Executive Reporting: Structured PowerPoint deck with paired visual/insight slides and policy framing

Impact

• Identifies high-risk zones for food insecurity and poverty burden
• Enables targeted policy response and scalable humanitarian monitoring
• Provides a replicable analytics framework for other regions or sectors

Folder structure

Humanitarian_Risk_Analytics_India/
├── data/              # Raw and cleaned datasets
├── sql/               # SQL scripts
├── python/            # Jupyter notebooks
├── Power_BI/          # Power BI pdf or .pbix file
├── Screenshots/       # Screenshots of dashboard (png)
├── report/            # Final PowerPoint deck (PPTX and PDF)
├── README.md          # Project summary